X-ray Phase-Contrast Radiography and Tomography with a Multiaperture Analyzer.

We present a multiaperture analyzer setup for performing x-ray phase contrast imaging in planar and three-dimensional modalities. The method is based on strongly structuring the x-ray beam with an amplitude modulator, before it reaches the sample, and on a multiaperture analyzing element before detection. A multislice representation of the sample is used to establish a quantitative relation between projection images and the corresponding three-dimensional distributions, leading to successful tomographic reconstruction. Sample absorption, phase, and scattering are retrieved from the measurement of five intensity projections. The method is tested on custom-built phantoms with synchrotron radiation: sample absorption and phase can be reliably retrieved also in combination with strong scatterers, simultaneously attaining high sensitivity and dynamic range.

Enhanced detection of threat materials by dark-field x-ray imaging combined with deep neural networks.

X-ray imaging has been boosted by the introduction of phase-based methods. Detail visibility is enhanced in phase contrast images, and dark-field images are sensitive to inhomogeneities on a length scale below the system's spatial resolution. Here we show that dark-field creates a texture which is characteristic of the imaged material, and that its combination with conventional attenuation leads to an improved discrimination of threat materials. We show that remaining ambiguities can be resolved by exploiting the different energy dependence of the dark-field and attenuation signals. Furthermore, we demonstrate that the dark-field texture is well-suited for identification through machine learning approaches through two proof-of-concept studies. In both cases, application of the same approaches to datasets from which the dark-field images were removed led to a clear degradation in performance. While the small scale of these studies means further research is required, results indicate potential for a combined use of dark-field and deep neural networks in security applications and beyond.

Note: Design and realization of a portable edge illumination X-ray phase contrast imaging system.

We discuss a portable edge illumination x-ray phase contrast imaging system based on compact piezoelectric motors, which enables its transportation to different environments, e.g., hosting different x-ray source technologies. The analysis of images of standard samples reveals an angular sensitivity of 270 ± 6 nrad, which compares well with the 260 ± 10 nrad reported for previous systems based on stepper motors, demonstrating that system portability can be achieved without affecting phase sensitivity. The results can also be considered a test of the performance of the piezoelectric motors, and as such could be of interest to researchers planning their use in other imaging systems.